


import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest
class 		__magic_name__						(snake_case_				):





     '''simple docstring'''



     __lowercase				:							Dict	   =       (UniPCMultistepScheduler,)
     __lowercase				:							Dict	   =       (('num_inference_steps', 25),)

     def 	SCREAMING_SNAKE_CASE__						(					self:Dict     ,		**_a:int   ):
       snake_case__						= {
           '''num_train_timesteps''': 10_00,
           '''beta_start''': 0.0001,
           '''beta_end''': 0.02,
           '''beta_schedule''': '''linear''',
           '''solver_order''': 2,
           '''solver_type''': '''bh2''',
       }

       config.update(**_a   )
       return config

     def 	SCREAMING_SNAKE_CASE__						(					self:str     ,		_a:Dict=0     ,		**_a:Tuple   ):
       snake_case__						= dict(self.forward_default_kwargs   )
       snake_case__						= kwargs.pop('''num_inference_steps'''     ,		_a   )
       snake_case__						= self.dummy_sample
       snake_case__						= 0.1 * sample
       snake_case__						= [residual + 0.2, residual + 0.15, residual + 0.10]

       for scheduler_class in self.scheduler_classes:
         snake_case__						= self.get_scheduler_config(**_a   )
         snake_case__						= scheduler_class(**_a   )
         scheduler.set_timesteps(_a   )
         # copy over dummy past residuals
         snake_case__						= dummy_past_residuals[: scheduler.config.solver_order]

         with tempfile.TemporaryDirectory() as tmpdirname:
           scheduler.save_config(_a   )
           snake_case__						= scheduler_class.from_pretrained(_a   )
           new_scheduler.set_timesteps(_a   )
           # copy over dummy past residuals
           snake_case__						= dummy_past_residuals[: new_scheduler.config.solver_order]

         snake_case__				,				snake_case__						= sample, sample
         for t in range(_a     ,		time_step + scheduler.config.solver_order + 1   ):
           snake_case__						= scheduler.step(_a     ,		_a     ,		_a     ,		**_a   ).prev_sample
           snake_case__						= new_scheduler.step(_a     ,		_a     ,		_a     ,		**_a   ).prev_sample

           assert torch.sum(torch.abs(output - new_output   )   ) < 1e-5, "Scheduler outputs are not identical"

     def 	SCREAMING_SNAKE_CASE__						(					self:Dict     ,		_a:str=0     ,		**_a:List[str]   ):
       snake_case__						= dict(self.forward_default_kwargs   )
       snake_case__						= kwargs.pop('''num_inference_steps'''     ,		_a   )
       snake_case__						= self.dummy_sample
       snake_case__						= 0.1 * sample
       snake_case__						= [residual + 0.2, residual + 0.15, residual + 0.10]

       for scheduler_class in self.scheduler_classes:
         snake_case__						= self.get_scheduler_config()
         snake_case__						= scheduler_class(**_a   )
         scheduler.set_timesteps(_a   )

         # copy over dummy past residuals (must be after setting timesteps)
         snake_case__						= dummy_past_residuals[: scheduler.config.solver_order]

         with tempfile.TemporaryDirectory() as tmpdirname:
           scheduler.save_config(_a   )
           snake_case__						= scheduler_class.from_pretrained(_a   )
           # copy over dummy past residuals
           new_scheduler.set_timesteps(_a   )

           # copy over dummy past residual (must be after setting timesteps)
           snake_case__						= dummy_past_residuals[: new_scheduler.config.solver_order]

         snake_case__						= scheduler.step(_a     ,		_a     ,		_a     ,		**_a   ).prev_sample
         snake_case__						= new_scheduler.step(_a     ,		_a     ,		_a     ,		**_a   ).prev_sample

         assert torch.sum(torch.abs(output - new_output   )   ) < 1e-5, "Scheduler outputs are not identical"

     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple     ,		_a:List[Any]=None     ,		**_a:List[str]   ):
       if scheduler is None:
         snake_case__						= self.scheduler_classes[0]
         snake_case__						= self.get_scheduler_config(**_a   )
         snake_case__						= scheduler_class(**_a   )

       snake_case__						= self.scheduler_classes[0]
       snake_case__						= self.get_scheduler_config(**_a   )
       snake_case__						= scheduler_class(**_a   )

       snake_case__						= 10
       snake_case__						= self.dummy_model()
       snake_case__						= self.dummy_sample_deter
       scheduler.set_timesteps(_a   )

       for i, t in enumerate(scheduler.timesteps   ):
         snake_case__						= model(_a     ,		_a   )
         snake_case__						= scheduler.step(_a     ,		_a     ,		_a   ).prev_sample

       return sample

     def 	SCREAMING_SNAKE_CASE__						(					self:int   ):
       snake_case__						= dict(self.forward_default_kwargs   )

       snake_case__						= kwargs.pop('''num_inference_steps'''     ,		_a   )

       for scheduler_class in self.scheduler_classes:
         snake_case__						= self.get_scheduler_config()
         snake_case__						= scheduler_class(**_a   )

         snake_case__						= self.dummy_sample
         snake_case__						= 0.1 * sample

         if num_inference_steps is not None and hasattr(_a     ,		'''set_timesteps'''   ):
           scheduler.set_timesteps(_a   )
         elif num_inference_steps is not None and not hasattr(_a     ,		'''set_timesteps'''   ):
           snake_case__						= num_inference_steps

         # copy over dummy past residuals (must be done after set_timesteps)
         snake_case__						= [residual + 0.2, residual + 0.15, residual + 0.10]
         snake_case__						= dummy_past_residuals[: scheduler.config.solver_order]

         snake_case__						= scheduler.timesteps[5]
         snake_case__						= scheduler.timesteps[6]

         snake_case__						= scheduler.step(_a     ,		_a     ,		_a     ,		**_a   ).prev_sample
         snake_case__						= scheduler.step(_a     ,		_a     ,		_a     ,		**_a   ).prev_sample

         self.assertEqual(output_a.shape     ,		sample.shape   )
         self.assertEqual(output_a.shape     ,		output_a.shape   )

     def 	SCREAMING_SNAKE_CASE__						(					self:int   ):
       # make sure that iterating over schedulers with same config names gives same results
       # for defaults
       snake_case__						= UniPCMultistepScheduler(**self.get_scheduler_config()   )
       snake_case__						= self.full_loop(scheduler=_a   )
       snake_case__						= torch.mean(torch.abs(_a   )   )

       assert abs(result_mean.item() - 0.2464   ) < 1e-3

       snake_case__						= DPMSolverSinglestepScheduler.from_config(scheduler.config   )
       snake_case__						= DEISMultistepScheduler.from_config(scheduler.config   )
       snake_case__						= DPMSolverMultistepScheduler.from_config(scheduler.config   )
       snake_case__						= UniPCMultistepScheduler.from_config(scheduler.config   )

       snake_case__						= self.full_loop(scheduler=_a   )
       snake_case__						= torch.mean(torch.abs(_a   )   )

       assert abs(result_mean.item() - 0.2464   ) < 1e-3

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]   ):
       for timesteps in [25, 50, 1_00, 9_99, 10_00]:
         self.check_over_configs(num_train_timesteps=_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]   ):
       self.check_over_configs(thresholding=_a   )
       for order in [1, 2, 3]:
         for solver_type in ["bh1", "bh2"]:
           for threshold in [0.5, 1.0, 2.0]:
             for prediction_type in ["epsilon", "sample"]:
               self.check_over_configs(
                   thresholding=_a     ,		prediction_type=_a     ,		sample_max_value=_a     ,		solver_order=_a     ,		solver_type=_a     ,		)

     def 	SCREAMING_SNAKE_CASE__						(					self:int   ):
       for prediction_type in ["epsilon", "v_prediction"]:
         self.check_over_configs(prediction_type=_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]   ):
       for solver_type in ["bh1", "bh2"]:
         for order in [1, 2, 3]:
           for prediction_type in ["epsilon", "sample"]:
             self.check_over_configs(
                 solver_order=_a     ,		solver_type=_a     ,		prediction_type=_a     ,		)
             snake_case__						= self.full_loop(
                 solver_order=_a     ,		solver_type=_a     ,		prediction_type=_a     ,		)
             assert not torch.isnan(_a   ).any(), "Samples have nan numbers"

     def 	SCREAMING_SNAKE_CASE__						(					self:Any   ):
       self.check_over_configs(lower_order_final=_a   )
       self.check_over_configs(lower_order_final=_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]   ):
       for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
         self.check_over_forward(num_inference_steps=_a     ,		time_step=0   )

     def 	SCREAMING_SNAKE_CASE__						(					self:str   ):
       snake_case__						= self.full_loop()
       snake_case__						= torch.mean(torch.abs(_a   )   )

       assert abs(result_mean.item() - 0.2464   ) < 1e-3

     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]   ):
       snake_case__						= self.full_loop(prediction_type='''v_prediction'''   )
       snake_case__						= torch.mean(torch.abs(_a   )   )

       assert abs(result_mean.item() - 0.1014   ) < 1e-3

     def 	SCREAMING_SNAKE_CASE__						(					self:List[str]   ):
       snake_case__						= self.scheduler_classes[0]
       snake_case__						= self.get_scheduler_config(thresholding=_a     ,		dynamic_thresholding_ratio=0   )
       snake_case__						= scheduler_class(**_a   )

       snake_case__						= 10
       snake_case__						= self.dummy_model()
       snake_case__						= self.dummy_sample_deter.half()
       scheduler.set_timesteps(_a   )

       for i, t in enumerate(scheduler.timesteps   ):
         snake_case__						= model(_a     ,		_a   )
         snake_case__						= scheduler.step(_a     ,		_a     ,		_a   ).prev_sample

       assert sample.dtype == torch.floataa







     def 	SCREAMING_SNAKE_CASE__						(					self:List[str]     ,		**_a:List[str]   ):
       for scheduler_class in self.scheduler_classes:
         snake_case__						= self.get_scheduler_config(**_a   )
         snake_case__						= scheduler_class(**_a   )

         scheduler.set_timesteps(scheduler.config.num_train_timesteps   )
         assert len(scheduler.timesteps.unique()   ) == scheduler.num_inference_steps



import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow


if is_tf_available():
		import tensorflow as tf

if is_tensorflow_text_available():
		from transformers.models.bert import TFBertTokenizer


lowercase    :   Union[str, Any]						=			['bert-base-uncased', 'bert-base-cased']
lowercase    :   Any						=			'hf-internal-testing/tiny-bert-tf-only'

if is_tf_available():




		class 					lowerCamelCase__				(							tf.keras.Model):




			'''simple docstring'''




			def __init__(   self						:List[Any]	,  a						:int		)						->					int:
										super().__init__()
										__UpperCamelCase      :  Any       =   tokenizer
										__UpperCamelCase      :  Union[str, Any]       =   AutoConfig.from_pretrained(a		)
										__UpperCamelCase      :  Dict       =   TFAutoModel.from_config(a		)







			def   _lowerCamelCase							(   self						:List[Any]	,  a						:Dict		)						->					Any:
										__UpperCamelCase      :  int       =   self.tokenizer(a		)
										__UpperCamelCase      :  int       =   self.bert(**a		)
										return out["pooler_output"]




@require_tf
@require_tensorflow_text
class 					lowerCamelCase__				(							unittest.TestCase):




	'''simple docstring'''




	def   _lowerCamelCase							(   self						:str		)						->					str:
								super().setUp()

								__UpperCamelCase      :  Dict       =   [
								    BertTokenizer.from_pretrained(a		) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
								]  # repeat for when fast_bert_tokenizer=false
								__UpperCamelCase      :  List[Any]       =   [TFBertTokenizer.from_pretrained(a		) for checkpoint in TOKENIZER_CHECKPOINTS] + [
								    TFBertTokenizer.from_pretrained(a	,  use_fast_bert_tokenizer=a		)
								    for checkpoint in TOKENIZER_CHECKPOINTS
								]
								assert len(self.tokenizers		) == len(self.tf_tokenizers		)

								__UpperCamelCase      :  Dict       =   [
								    "This is a straightforward English test sentence.",
								    "This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.",
								    "Now we're going to add some Chinese: 一 二 三 一二三",
								    "And some much more rare Chinese: 齉 堃 齉堃",
								    "Je vais aussi écrire en français pour tester les accents",
								    "Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ",
								]
								__UpperCamelCase      :  Dict       =   list(zip(self.test_sentences	,  self.test_sentences[::-1]		)		)







	def   _lowerCamelCase							(   self						:List[Any]		)						->					int:
								for tokenizer, tf_tokenizer in zip(self.tokenizers	,  self.tf_tokenizers		):
															for test_inputs in (self.test_sentences, self.paired_sentences):
																						__UpperCamelCase      :  str       =   tokenizer(a	,  return_tensors="tf"	,  padding="longest"		)
																						__UpperCamelCase      :  Any       =   tf_tokenizer(a		)

																						for key in python_outputs.keys():
																													self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape		)		)
																													self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key]	,  tf.intaa		) == tf_outputs[key]		)		)







	@slow
	def   _lowerCamelCase							(   self						:Union[str, Any]		)						->					Any:
								for tf_tokenizer in self.tf_tokenizers:
															__UpperCamelCase      :  List[str]       =   tf_tokenizer(self.paired_sentences		)
															__UpperCamelCase      :  Union[str, Any]       =   tf_tokenizer(
															    text=[sentence[0] for sentence in self.paired_sentences]	,  text_pair=[sentence[1] for sentence in self.paired_sentences]	,  )
															for key in merged_outputs.keys():
																						self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key]	,  tf.intaa		) == separated_outputs[key]		)		)







	@slow
	def   _lowerCamelCase							(   self						:Any		)						->					Optional[int]:
								for tf_tokenizer in self.tf_tokenizers:
															__UpperCamelCase      :  Optional[Any]       =   tf.function(a		)
															for test_inputs in (self.test_sentences, self.paired_sentences):
																						__UpperCamelCase      :  Tuple       =   tf.constant(a		)
																						__UpperCamelCase      :  List[str]       =   compiled_tokenizer(a		)
																						__UpperCamelCase      :  Any       =   tf_tokenizer(a		)

																						for key in eager_outputs.keys():
																													self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]		)		)







	@slow
	def   _lowerCamelCase							(   self						:List[Any]		)						->					Tuple:
								for tf_tokenizer in self.tf_tokenizers:
															__UpperCamelCase      :  str       =   ModelToSave(tokenizer=a		)
															__UpperCamelCase      :  List[Any]       =   tf.convert_to_tensor(self.test_sentences		)
															__UpperCamelCase      :  Optional[int]       =   model(a		)  # Build model with some sample inputs
															with TemporaryDirectory() as tempdir:
																						__UpperCamelCase      :  Optional[Any]       =   Path(a		) / "saved.model"
																						model.save(a		)
																						__UpperCamelCase      :  List[Any]       =   tf.keras.models.load_model(a		)
															__UpperCamelCase      :  Optional[Any]       =   loaded_model(a		)
															# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
															self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output		)		)	,  1E-5		)